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Saving the World with Poop · What's Your Problem?

0:15
Speaker 1: Pushkin. If you took a COVID test recently, you probably
0:23
Speaker 1: took it at home, and if you tested positive, you
0:26
Speaker 1: probably didn't report the results to your local health department,
0:29
Speaker 1: which you know fair enough, no judgment. But in the
0:32
Speaker 1: big picture this does create a problem. How do we
0:36
Speaker 1: keep track of COVID levels as public testing declines. How
0:40
Speaker 1: do we look out for new variants? How do we
0:42
Speaker 1: monitor new outbreaks? The answer, in a word, is poop.
0:52
Speaker 1: I'm Jacob Goldstein and this is what's your problem. My
0:55
Speaker 1: guest today is Mariana Matus, co founder and CEO of
0:59
Speaker 1: biobot Analytics. The company analyzes sewage samples from hundreds of
1:04
Speaker 1: water treatment plants across the US and Canada. They provide
1:07
Speaker 1: an aggregate picture of the cod status of tens of
1:10
Speaker 1: millions of people. Marianna's problem is this, how do you
1:15
Speaker 1: move beyond COVID to turn raw sewage into data that
1:18
Speaker 1: can help improve global public health. As it's become clear
1:24
Speaker 1: that sampling sewage is one of the best ways to
1:26
Speaker 1: track the COVID pandemic, the field of wastewater epidemiology has
1:30
Speaker 1: become a big deal. But when Marianna discovered the field,
1:33
Speaker 1: it wasn't particularly popular or flashy. It was years before
1:36
Speaker 1: the pandemic when she was working on her PhD and
1:39
Speaker 1: computational biology at MIT. But Marianna told me studying wastewater
1:45
Speaker 1: seemed like a great fit for what she wanted to do.
1:48
Speaker 1: When I came across this space of voicewatermology through through
1:52
Speaker 1: my laboratory, it was perfect, I think for me as
1:56
Speaker 1: a combination of cutting edge science but also like a
2:01
Speaker 1: very big societal impact. When you imagined having a big
2:06
Speaker 1: societal impact through your study of wastewater epidemiology, like what
2:11
Speaker 1: did you imagine? What was like your big, big dream.
2:15
Speaker 1: I grew up in Mexico City and in a you know,
2:18
Speaker 1: in a very sort of like slummy part of Mexico City,
2:21
Speaker 1: not on the nice part of Mexico City. So I
2:25
Speaker 1: grew up basically with zero government resources, you know, in
2:33
Speaker 1: areas that are ignored and that don't receive any sort
2:37
Speaker 1: of like government resources. And I grew up thinking that
2:41
Speaker 1: life was like that. And then my career took me
2:45
Speaker 1: to study in great universities and places, you know, in
2:49
Speaker 1: the Netherlands, in the UK, in Boston, and then I
2:52
Speaker 1: just began to realize that it actually it's not like that.
2:56
Speaker 1: Not every places like that, And to me, you know,
2:59
Speaker 1: I think like the more that dug into it through
3:01
Speaker 1: my peach D studies, like I realized that a big
3:05
Speaker 1: part of the solution starts with collecting the data to
3:10
Speaker 1: demonstrate those health disparities that you know, I know that
3:14
Speaker 1: are out there and everybody sort of like knows that
3:16
Speaker 1: are out there. But it's just hard to to really
3:19
Speaker 1: take action when you just don't have it in your
3:22
Speaker 1: face and not don't have a way to measure progress.
3:25
Speaker 1: So what I would imagine is, how can we use
3:29
Speaker 1: the data from the waste water to give everybody a
3:32
Speaker 1: voice and to create maps of what's happening near real
3:37
Speaker 1: time everywhere and to and to design action around that data.
3:43
Speaker 1: How did you go from that initial kind of big
3:45
Speaker 1: dream to starting a company? So when I came to MT,
3:49
Speaker 1: it sort of like I had all these dreams in
3:51
Speaker 1: my head about like, oh, imagine using science to you know,
3:56
Speaker 1: just impact society. Wouldn't that be amazing? But I didn't
4:00
Speaker 1: know what that meant in practice. And when I came
4:03
Speaker 1: to MT, I discovered entrepreneurship. Entrepreneurship, h yes, I mean
4:09
Speaker 1: just like the concept that anybody can be a founder
4:13
Speaker 1: and start their own company, right, Like, just that concept,
4:16
Speaker 1: I would believe it or not. I mean, I just
4:18
Speaker 1: didn't know about that before I came to a m
4:21
Speaker 1: I T. I always thought only people that already have
4:24
Speaker 1: family money, or like you know that studied business for underground,
4:29
Speaker 1: they are the ones who can start businesses. And just
4:32
Speaker 1: that concept completely blew my mind. And I think it
4:35
Speaker 1: was for me like the missing piece. I realized, Oh, wow, well,
4:39
Speaker 1: then that's how I do it. That's how I create
4:41
Speaker 1: a company based on science to go and solve a
4:43
Speaker 1: big problem. One thing I heard you talking about and
4:47
Speaker 1: another interview was when you were starting the company, people
4:52
Speaker 1: seem to misunderstand and think that you should be starting
4:57
Speaker 1: a nonprofit instead of a company. Is that right and
4:59
Speaker 1: if so, can you tell me about that? Absolutely so.
5:04
Speaker 1: The idea of selling to government, the idea of of
5:09
Speaker 1: operating in the space of public health, and the idea
5:14
Speaker 1: of having two women co founders out of MIT basically
5:19
Speaker 1: two very technical women co founders. Kind of it was
5:22
Speaker 1: a kind of a perfect trifecta for investors to think
5:26
Speaker 1: about US as a nonprofit. You mentioned gender, You mentioned
5:31
Speaker 1: the fact that that you and your co founder are
5:33
Speaker 1: both women, and that that played into it. How did that?
5:36
Speaker 1: How did that work? What was going on there? Yeah,
5:41
Speaker 1: it seems that we you know, I had to be
5:44
Speaker 1: very proactive about kind of explaining I'm doing this not
5:50
Speaker 1: just to have public impact, not just to have a
5:54
Speaker 1: social impact. I'm doing this because I'm also looking to
5:58
Speaker 1: have a financial outcome. And I just found it funny
6:02
Speaker 1: that I had to be saying like, hey, I want
6:05
Speaker 1: to make money, like you know, I personally also want
6:08
Speaker 1: to make any Why wouldn't I? Right, So, okay, so
6:13
Speaker 1: so you found the company. So so let's jump to
6:17
Speaker 1: say twenty nineteen, like beginning of twenty twenty. Now you
6:21
Speaker 1: have this little company that's been up and running for
6:23
Speaker 1: a couple of years. It's analyzing wastewater, but you have
6:27
Speaker 1: no revenue. You have a staff of just like five people.
6:30
Speaker 1: You built this little machine that goes into the sewer
6:32
Speaker 1: to collect sewage, and you're working on one pilot project.
6:36
Speaker 1: And that project isn't even a fur infectious disease. Right,
6:39
Speaker 1: You're looking at opioids, You're you're sort of studying the
6:42
Speaker 1: opioid epidemic. And yet this is this moment when history
6:47
Speaker 1: is like barreling towards you. Right, this pandemic is about
6:50
Speaker 1: to you know, change everything, right, And so I'm curious
6:54
Speaker 1: when did you start to hear about, you know, about
6:56
Speaker 1: this new coronavirus outbreak. Yeah, I think for us, UM
7:00
Speaker 1: it really began to catch our attention in like early
7:06
Speaker 1: twenty twenty, like January twenty twenty. Do you remember any
7:10
Speaker 1: particular moments, any particular conversations or reading anything or talking
7:14
Speaker 1: to anybody. I mean definitely, so some of those like
7:19
Speaker 1: first conversations that happened where we had been forming a
7:23
Speaker 1: relationship with folks within HHS, like the federal HHS Department
7:29
Speaker 1: of Health and Human Services, the big federal Apartment of
7:31
Speaker 1: Health and Human Services in DC. So I remember we
7:35
Speaker 1: were in DC in February of twenty twenty, basically like
7:40
Speaker 1: already pitching that we should begin to test the waste
7:43
Speaker 1: water of all of the major airports across the country
7:46
Speaker 1: to start getting a sense of the level of import
7:50
Speaker 1: And we were told, well, this outbreak is going to
7:54
Speaker 1: be contained within two weeks, so it would be a
7:58
Speaker 1: waste of your resources to try to develop a solution
8:01
Speaker 1: around this now because you're not going to be ready
8:03
Speaker 1: in less than two weeks. They were like, by the
8:05
Speaker 1: time you figure out how to do this test in
8:07
Speaker 1: a few weeks, this all things going to have blown over.
8:09
Speaker 1: It's not going exactly so. But despite that sort of
8:13
Speaker 1: like very strong feedback, we came back from DC and
8:17
Speaker 1: we this was February of twenty twenty, and we make
8:20
Speaker 1: the decision that we were going to build a COVID
8:23
Speaker 1: solution based on the waste water and tell me about
8:26
Speaker 1: building that. We had to basically change everything. We had
8:30
Speaker 1: to throw away sort of like all of the progress
8:35
Speaker 1: that we had made up until that point to start
8:38
Speaker 1: over given the constraints at hand. Number one, this was
8:43
Speaker 1: there was a sense of emergency and that time was
8:47
Speaker 1: of the essence. So the idea of having to manufacture
8:53
Speaker 1: hardware in order to collect data at scale just seemed
8:59
Speaker 1: pretty hard or impossible in that moment. Also, do you
9:01
Speaker 1: know the supply chain issues all of You're not going
9:04
Speaker 1: to be able to make this special device. You're not
9:05
Speaker 1: gonna be only like a thousand of them in a
9:07
Speaker 1: couple of months or whatever, exactly exactly, So we decided, okay,
9:11
Speaker 1: where how else can we get wastewater easily? And we realize, oh,
9:16
Speaker 1: actually actually, there are sixteen thousand wastewater treatment plants in
9:22
Speaker 1: the US, covering eighty percent of the US population, and
9:26
Speaker 1: turns out most of them already have a similar type
9:30
Speaker 1: of equipment. So it's not portable, it's not as sophisticated,
9:35
Speaker 1: but it does the job at collecting that continues twenty
9:38
Speaker 1: four hour sample. The minimum viable product already exists at
9:42
Speaker 1: sixteen thousand wastewater plants all across the US, so we
9:45
Speaker 1: were like, okay, check. Second was the lab component. We
9:50
Speaker 1: had been building a chemistry platform, and obviously and COVID
9:55
Speaker 1: is caused by an RNA virus, so the detection or
10:00
Speaker 1: detest in the lab would need to be more of
10:03
Speaker 1: a molecular biology type of approach. We didn't have any
10:06
Speaker 1: of those capabilities, but we had raised seat round, like
10:12
Speaker 1: a you know, a four point two million dollars seat round,
10:16
Speaker 1: and so we had some money to just start getting
10:19
Speaker 1: some basic new equipment in the lab. And also we
10:23
Speaker 1: have a good relationship with my PhD advisor at MT
10:28
Speaker 1: so together with him, we actually used a lot of
10:33
Speaker 1: his lab early on in the pandemic and collaborated with
10:36
Speaker 1: his students. So you know, that's how we got started,
10:39
Speaker 1: and it took us like four weeks to just go
10:46
Speaker 1: from the decision point until having actually not just a
10:50
Speaker 1: proof that the test works with waste water, but also
10:57
Speaker 1: we wrote a scientific preprint, like a you know, a paper.
11:02
Speaker 1: So we were the first ones in the country to
11:05
Speaker 1: demonstrate that you can detect and quantify the virus causing
11:10
Speaker 1: COVID nineteen in wastewater samples and that it works well.
11:15
Speaker 1: And I think, you know, it was just from there
11:18
Speaker 1: that everything exploded. I mean, is there fear I assume
11:21
Speaker 1: that that there's no risk of transmitting the disease. I mean,
11:25
Speaker 1: it's obviously not transmitted through wastewater, but we're people afraid
11:30
Speaker 1: that it might be. We didn't know anything about COVID
11:32
Speaker 1: at the time. I mean, this is when I was
11:33
Speaker 1: still wiping off all my groceries with bleach, right, because
11:37
Speaker 1: literally we didn't know. Oh yes, there was a lot
11:40
Speaker 1: of concern. And actually that was you know, thank you
11:43
Speaker 1: for I feel like my brain probably like tried to
11:46
Speaker 1: forget that, but that was a very big deal. Like
11:50
Speaker 1: when we just started from for many reasons, nobody knew
11:54
Speaker 1: if the virus present in wastewater would be infectious. My
11:59
Speaker 1: very small team that we had. They were really afraid
12:03
Speaker 1: to have to handle the wastewater sample without knowing if
12:06
Speaker 1: they can get infected. So we came up with a
12:10
Speaker 1: solution for our lab where every every bottle of wastewater,
12:15
Speaker 1: example that arrives to buy a boat is first pasteurized
12:20
Speaker 1: in order to basically make it non infectious or like sterile. Yes.
12:26
Speaker 1: And the third thing was that I had just I
12:28
Speaker 1: was just pregnant at the time. I became pregnant in
12:32
Speaker 1: March of twenty twenty, my first pregnancy, my first you
12:36
Speaker 1: know baby, and I my husband and I also would
12:40
Speaker 1: go into the lab to model to the rest of
12:43
Speaker 1: our team that you know this is safe, Like you know,
12:47
Speaker 1: I'm pregnant, but I trust that the pasteurization is good
12:53
Speaker 1: enough to make this work safe and let me, you know,
12:56
Speaker 1: let me demonstrate that with my actions. At the time,
12:59
Speaker 1: it was a very big deal. So you have this idea,
13:03
Speaker 1: you have the proof that it works, You've convinced the
13:06
Speaker 1: staff at your company, at Bio Boat, that it's okay
13:08
Speaker 1: to do this. How do you get customers what happens next?
13:12
Speaker 1: So we designed a probono campaign where by About would
13:15
Speaker 1: absorb the cost of all of the testing for two months,
13:21
Speaker 1: and what happened it was beyond our wildest dreams, Like
13:25
Speaker 1: it exploded. It got picked up by the media. People
13:30
Speaker 1: loft this idea that you can fight COVID with poop. Poop. Yeah,
13:38
Speaker 1: you can fight COVID with poop. You know. That was
13:40
Speaker 1: that was basically the message being put out there. It
13:43
Speaker 1: went viral and we got in just the first ten
13:48
Speaker 1: days or so of announcing the campaign, we got four
13:52
Speaker 1: hundred plants nationwide wanting to enroll in the program. We
13:58
Speaker 1: had aspired to enroll up to one hundred, So it
14:03
Speaker 1: was it was crazy. The response was just crazy. But
14:07
Speaker 1: you know, that transition was tough because I guess also
14:09
Speaker 1: something that I haven't talked about is that in that
14:13
Speaker 1: early twenty twenty stage, when we were the first ones,
14:17
Speaker 1: the more established academics were skeptical and basically like recommending
14:24
Speaker 1: caution with these and slowing things down for us. And
14:29
Speaker 1: so it was a very Were they skeptical of wastewater
14:32
Speaker 1: sampling in general or were they skeptical of your company?
14:37
Speaker 1: Or was it both both both? So, up until twenty twenty,
14:45
Speaker 1: the wastewater epidemiology was a very niche and obscure area
14:51
Speaker 1: of science, So the entire field was a little bit like,
14:56
Speaker 1: you know, not very well known obviously. And if that
15:01
Speaker 1: was within science, just imagine within government, you know, like
15:04
Speaker 1: it was just nobody was saying, oh, I need wastewater data,
15:08
Speaker 1: like who can provide it for me? Why do you
15:10
Speaker 1: think that is? I mean, do you think it's partly
15:12
Speaker 1: like because it's poop, because it's sewage? Like why do
15:16
Speaker 1: you think people were ignoring this source of data? Really?
15:20
Speaker 1: I mean, is it because it's it's poop and people
15:22
Speaker 1: are kind of embarrassed by it don't like to talk
15:24
Speaker 1: about it? Like has that been a barrier too? I
15:28
Speaker 1: mean I think that No. I think that actually people
15:31
Speaker 1: love all of the poop jokes and poop humor. I
15:35
Speaker 1: feel like that actually tends to be like a plus.
15:38
Speaker 1: How do you feel about them at this point? Like
15:40
Speaker 1: are you tired of them? Do they work in your favor?
15:43
Speaker 1: Like our poop jokes like enough, I never want to
15:45
Speaker 1: hear a poop joke again? No, no, no, I love it.
15:47
Speaker 1: I love it. And our company culture, you know, celebrates
15:50
Speaker 1: the poop humor quite a lot with okay, our you know,
15:55
Speaker 1: branding and suave and you know, we we love it.
15:59
Speaker 1: We love the users of the poop emoji or they're
16:01
Speaker 1: like particularly very big. Oh yeah, the poop emoji is
16:05
Speaker 1: the most use emoji in the bay about slack, it
16:09
Speaker 1: makes sense. I mean, it's why they're used even at
16:12
Speaker 1: places that have nothing to do with it. Right, So yes, no,
16:15
Speaker 1: I think that, you know, I think that the barrier
16:18
Speaker 1: to research and to you know, first to research and
16:22
Speaker 1: then to the adoption of the data is just that
16:26
Speaker 1: the bar to get the data in is very high
16:30
Speaker 1: in the sense that there's a lot of operational and
16:33
Speaker 1: logistical work in order to get data. So when you
16:38
Speaker 1: are working within a university or within a government agency,
16:43
Speaker 1: who really has the time to be you know, building
16:47
Speaker 1: kits to transport wastewater, learning how to the regulations to
16:51
Speaker 1: transport it going out there, or finding the relationships to
16:56
Speaker 1: send you the waste water in doing all of that
16:59
Speaker 1: groundwork in the lab, right groundwork. Right, It's very logistical.
17:03
Speaker 1: It's not like high minded, it's not fancy math. It's
17:06
Speaker 1: just getting appealed to the lab. Yeah, exactly, exactly. So
17:11
Speaker 1: when I was a PITCHD student that in my t
17:14
Speaker 1: I was basically like the only one like willing to
17:19
Speaker 1: do the groundwork because I started working on always what
17:24
Speaker 1: epidemiology during my PhD. That's what my pitchd PSIS was about.
17:28
Speaker 1: But you know, I really struggled with just like recruiting
17:32
Speaker 1: a team around me, because folks were wanting to help
17:37
Speaker 1: with the data analysis, the data visualization, but not the
17:41
Speaker 1: eighty percent groundwork that goes behind it. So nobody wants
17:45
Speaker 1: get the proof exactly. So that's why I thought, always thought,
17:49
Speaker 1: you know, if this science is going to really go mainstream,
17:57
Speaker 1: it needs an organization dedicated like one to it so
18:02
Speaker 1: that we can do that around work and then the scientists. Yes,
18:05
Speaker 1: of course, the power is in the data, in the
18:08
Speaker 1: insights that you can get out of it, but you
18:10
Speaker 1: know there's an eighty percent around work behind it. Well,
18:12
Speaker 1: I mean maybe that's part of the case for a
18:14
Speaker 1: for profit company, right Like profit is a very good
18:18
Speaker 1: incentive to do groundwork, absolutely absolutely Well. How big is
18:24
Speaker 1: the company now? Oh yeah, buy about now has one
18:28
Speaker 1: hundred employees. How much revenue do you have? More or less?
18:32
Speaker 1: This year? We are on track to make over twenty
18:37
Speaker 1: million dollars in revenue, okay, which is great also to
18:42
Speaker 1: be here given that two years ago we were basically
18:44
Speaker 1: pre revenue. So yes, two years ago was zero. From
18:47
Speaker 1: zero to twenty million, there's a lot. Are you profitable, No,
18:53
Speaker 1: not yet. I mean we are investing heavily in our
18:58
Speaker 1: R and D so that we can look at other
19:00
Speaker 1: types of data. So story so far. Three years ago,
19:07
Speaker 1: Mariana barely had a company, and waste water surveillance was
19:10
Speaker 1: this little niche thing in the US. And then by
19:14
Speaker 1: the beginning of this year, when the omicron wave hit
19:16
Speaker 1: the country, BAA was this big, real company. Wastewater surveillance
19:21
Speaker 1: was one of the most important tools public health officials
19:24
Speaker 1: used to figure out where cases were spiking, so that
19:27
Speaker 1: they could direct resources into the right neighborhoods and you know,
19:30
Speaker 1: plan for surges and hospital admissions. And the story is
19:33
Speaker 1: not over in a minute. What we still don't know
19:37
Speaker 1: about poop and disease. It's a lot. Now back to
19:48
Speaker 1: the show. And one thing I'm curious about, Well, what
19:52
Speaker 1: is your company's moat? Right? Like, how is what you
19:55
Speaker 1: do not just some commodity thing that anybody with the
19:58
Speaker 1: lab can do. Yeah, I think that that's a great question. Everybody, anybody,
20:04
Speaker 1: anybody out there could go and let's say open amunthole
20:10
Speaker 1: and get a bucket in pull up wastewater, take it
20:14
Speaker 1: to a lab and they would get a number, a
20:18
Speaker 1: number meaning like an amount of COVID that's in there
20:21
Speaker 1: or something. You may get an amount of COVID, you make,
20:23
Speaker 1: an amount of opioids, an amount of influenza, right like
20:27
Speaker 1: any any number. You may get a number, But what
20:30
Speaker 1: does that number mean? Like that number in isolation really
20:36
Speaker 1: doesn't mean anything. So our secret sauce is how do
20:42
Speaker 1: we create how do we make wastewater a data platform?
20:48
Speaker 1: And that really means it's a systems level type of
20:54
Speaker 1: design and product. We actually need to use the same
20:58
Speaker 1: method in every sample. If we want to calculate, if
21:03
Speaker 1: we want to compare across time, if we want to
21:07
Speaker 1: compare across locations, we want to build a state statewide trend,
21:13
Speaker 1: if we want to compare regions, if we want to
21:16
Speaker 1: calculate a national average, it's imperative to actually use the
21:21
Speaker 1: same methods at every stage for every sample. Otherwise you
21:26
Speaker 1: don't have that comparability wastewater. Again, the power of the
21:30
Speaker 1: wastewater isn't about what you about the number that you
21:34
Speaker 1: get from a single wastewater sample. The power of wastewater
21:38
Speaker 1: is about having that bird's eye view, that systems level
21:43
Speaker 1: view of what's happening in an entire country, in an
21:46
Speaker 1: entire state. You can drill in more geographically narrow, but
21:51
Speaker 1: you can also zoom out. That's the platform, that's the
21:54
Speaker 1: power that we're building. So in order to accomplish that,
21:58
Speaker 1: you know, that's where we come in, all of the relationships,
22:01
Speaker 1: all of the scale that eight you know, groundwork that
22:07
Speaker 1: I talked about. To accomplish that level of visibility, that's
22:12
Speaker 1: a tough problem. So it's like a network effect. There's
22:14
Speaker 1: a network effect thing people would want to use your
22:17
Speaker 1: company because you have the most data and therefore you
22:20
Speaker 1: have the best understanding of what one particular sample means
22:24
Speaker 1: because you can put it in the context of all
22:26
Speaker 1: the other data that you have gotten, have processed exactly exactly.
22:30
Speaker 1: So we have the largest network of sites in the world.
22:36
Speaker 1: It's mostly US, some Canada. How big is it now?
22:41
Speaker 1: We have over five hundred sites. What's something you haven't
22:45
Speaker 1: figured out yet? What's a problem your story of still
22:48
Speaker 1: trying to figure out how to solve Maybe you've tried
22:50
Speaker 1: it hasn't quite worked yet. What's something you're still working
22:52
Speaker 1: on in the lab. There is a lot of interest
22:56
Speaker 1: in looking at different targets, so not just COVID, but
23:01
Speaker 1: also influenza, neural virus. You can you know, at least
23:06
Speaker 1: keeps going right like monkey box where right now in
23:10
Speaker 1: advanced R and D for monkey pops, we're gonna have
23:13
Speaker 1: a start testing with water for monkey pops too. But
23:16
Speaker 1: it remains a challenge to know kind of how to
23:21
Speaker 1: which types of data we can collect and how to
23:23
Speaker 1: interpret them because we don't really know which pathogens are
23:30
Speaker 1: shed in poop. Huh, that's surprising. I would think people
23:36
Speaker 1: would just sort of have figured that out by now.
23:38
Speaker 1: Is that Does that go back to the fact that
23:39
Speaker 1: it's kind of an understudied area exactly exactly. There's a
23:44
Speaker 1: lot of information about how you can diagnose a disease
23:48
Speaker 1: in a person via like a serom sample, via maybe
23:53
Speaker 1: a blood test, yeah, a blood test, or maybe a
23:57
Speaker 1: you know, a swab or like a you know, a
24:00
Speaker 1: nasal swab or a saliva suab sometimes in in p
24:05
Speaker 1: But but pooh, I mean, why would they have to
24:09
Speaker 1: I mean, the clinic the worst way to get us
24:12
Speaker 1: to a sample for the doctor, right exactly, So so
24:15
Speaker 1: for the doctor like you know, again, poop is an
24:18
Speaker 1: understudied like sort of like diagnostic matrix. So you know,
24:23
Speaker 1: one wild dream of mine is for us to develop
24:27
Speaker 1: the clinical collaborations to to be able to know in
24:31
Speaker 1: people like how different diseases are um shedding in poop,
24:36
Speaker 1: because that that will help us to understand which targets
24:39
Speaker 1: will be easier to track via the waste water and
24:44
Speaker 1: how to interpret it. You mentioned sort of starting out
24:47
Speaker 1: in the field with this the dream of being able
24:52
Speaker 1: to to look at a city like Mexico City and
24:57
Speaker 1: get you know, sort of a map of what's going
24:58
Speaker 1: on with the health of people in the city. How
25:02
Speaker 1: do you get to there from where you are now?
25:04
Speaker 1: Because we have done some pilots in Mexico Ecuador Ry
25:09
Speaker 1: in partnership already with a World Bank, so you know,
25:12
Speaker 1: it's kind of how do we build on top of
25:14
Speaker 1: those early pilots that were very successful. So you sample,
25:18
Speaker 1: you know, big sewage systems in the US that allows
25:22
Speaker 1: you to capture something like eighty percent of the population,
25:26
Speaker 1: but it seems like in large parts of the developing
25:29
Speaker 1: world you have places you know, that are less developed,
25:32
Speaker 1: where where there are not sewage systems. What do you
25:35
Speaker 1: do there. There's many areas that don't have any sort
25:39
Speaker 1: of sanitation, right, so I think that for those areas
25:45
Speaker 1: there's just immediate action, which is we can use basically
25:49
Speaker 1: the same type of sampling approaches to open sewers, like
25:54
Speaker 1: basically which are rivers where the waste makes it into
25:58
Speaker 1: the river, and that that is possible, that that works,
26:01
Speaker 1: that's how some people already do waste. What are epithimolity
26:04
Speaker 1: You work in areas without the infrastructure. But I think
26:07
Speaker 1: you're right at over time this technology could be a
26:13
Speaker 1: new reason to invest in waste, what in building the
26:17
Speaker 1: waste water infrastructure where it doesn't exist, because now we're
26:21
Speaker 1: giving it a second second use, a second mission. This
26:26
Speaker 1: is no longer just about the collection and safe disposal
26:29
Speaker 1: of waste from people. This is about intelligence that you
26:33
Speaker 1: cannot really get any in any other way. So hopefully
26:38
Speaker 1: it can be a virtual cycle where it can foster
26:43
Speaker 1: the investment in the building of this infrastructure, which would
26:47
Speaker 1: only of course also improve public health because then would
26:52
Speaker 1: reduce the spread of disease. In a minute, the lightning
26:59
Speaker 1: round with questions about immigration, entrepreneurship, and what not to
27:04
Speaker 1: flush down the toilet now back to the show. Let's
27:15
Speaker 1: just do let's do like a lightning round. I'm just
27:18
Speaker 1: gonna ask you a bunch of fast questions and you
27:20
Speaker 1: can answer them fast. Why do you think immigrants are
27:24
Speaker 1: more likely than native born citizens to become entrepreneurs? That's
27:30
Speaker 1: a good question, so I I mean, I can only
27:32
Speaker 1: speak about my kind of experience, but at least in
27:35
Speaker 1: my case, I felt that a little bit like I
27:39
Speaker 1: had nothing to lose, So why not try? Like, why
27:44
Speaker 1: wouldn't I try? What's one thing people should never ever
27:48
Speaker 1: flush down the toilet? Well, that's a great question. Never
27:59
Speaker 1: flushed down the so called flushable wipes okay, not actually
28:06
Speaker 1: flushable at do not actually flushable. They don't dissolve in water,
28:14
Speaker 1: So what happens is that they create this massive the
28:19
Speaker 1: glock systems everywhere. The Q tips are also pretty bad
28:23
Speaker 1: because they have size tends to be the same as
28:26
Speaker 1: like some of the sort of like filters that capture
28:31
Speaker 1: just like big, big junk out of the waste water,
28:33
Speaker 1: so they also block those systems. The Q tips are
28:37
Speaker 1: not great to fulush either. Feel like I touch the
28:41
Speaker 1: nerve here? Yes? Oh yeah? If everything goes well, what
28:47
Speaker 1: problem will you be trying to solve in five years,
28:50
Speaker 1: due to climate change, we can expect to see more
28:55
Speaker 1: infectious outbreaks than before in a more frequent manner. The
29:01
Speaker 1: patterns of you know, just wildlife of insects, factor insects
29:07
Speaker 1: are just changing, so so we can expect to see
29:12
Speaker 1: more zonoric events of infectious diseases jumping to humans. So,
29:18
Speaker 1: in my opinion, I think that the next five to
29:20
Speaker 1: ten years should be more about that international collaboration around
29:25
Speaker 1: health understanding. You know, what we do here won't be enough.
29:29
Speaker 1: We need to be connected. So I would love if
29:32
Speaker 1: we have been so successful at collecting, you know, at
29:35
Speaker 1: building this data asset and having the trust of different
29:40
Speaker 1: governments that we can even begin to be part of
29:43
Speaker 1: that collaboration. What's the most interesting thing you ever saw
29:48
Speaker 1: pulled out of a sewer? Oh? Wow, Yeah, that's another
29:52
Speaker 1: good one. Well, I mean I didn't pull this myself,
29:59
Speaker 1: but we were touring one of the plants that we
30:03
Speaker 1: work with in in Portland, Maine just a few weeks ago,
30:08
Speaker 1: and they were telling us that they recently pulled the
30:12
Speaker 1: door of a car. I don't know what happened, but
30:19
Speaker 1: there was a door of a car that they had
30:22
Speaker 1: to pull out of the stream. Marianna Matus is co
30:30
Speaker 1: founder and CEO of biobot Analytics. Today's show was produced
30:35
Speaker 1: by Edith Ruslow, engineered by Amanda ka Wong, and edited
30:39
Speaker 1: by Robert Smith. I'm Jacob Goldstein, and we'll be back
30:42
Speaker 1: next week with another episode of What's Your Problem